A biologically-inspired benchmark for the evaluation of molecular generative models
A biologically-inspired evaluation of molecular generative machine learning
Generative models have recently become ubiquitous in many scientific areas, yet less attention has been paid to their evaluation.
In this study, a biologically-inspired benchmark for the evaluation of molecular generative models is proposed.
Specifically, three diverse reference datasets are designed and a set of metrics are introduced which are directly relevant to the drug discovery process.
In particular we propose a recreation metric and apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs.
While all three metrics show consistent results across the tested generative models, a more detailed comparison of drug-target affinity binding and molecular docking scores revealed that unimodal predictiors can lead to erroneous conclusions about target binding on a molecular level and a multi-modal approach is thus preferredrable.